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Author: Liao Wang
Publisher:
ISBN:
Category :
Languages : en
Pages : 0
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Book Description
A central problem in planning production capacity is how to effectively manage demand risk. We develop a model that integrates capacity planning and risk hedging decisions under a popular risk measure, conditional value at risk (CVaR). The CVaR objective generalizes the usual risk-neutral objective (such as the expected payoff), and allows for explicit modeling of the degree of aversion to downside risk (associated with low demand). The starting point of our model is to incorporate the impact on demand from a financial asset (including, for instance, a tradable market index as a proxy for the general economy) via a demand rate function. This way, in addition to the capacity decision at the beginning of the planning horizon, there is also a dynamic hedging strategy throughout the horizon, the latter plays the role of both mitigating demand risk and supplementing the payoff. The hedging strategy is restricted to partial information along with a cap on loss (pathwise). To find the optimal hedging strategy, we construct and solve a dual problem to derive the optimal terminal wealth from hedging; the real-time hedging strategy is then mapped out via the martingale representation theorem. With the hedging strategy optimized, we show that optimizing the production quantity is a concave maximization problem. With both production and hedging (jointly) optimized, we provide a complete characterization of the efficient frontier, and quantify the improvement over the production-only approach. Furthermore, via sensitivity and asymptotic analyses, we spell out the impacts of the hedging budget and the risk aversion level, along with other qualitative insights.
Author: Liao Wang
Publisher:
ISBN:
Category :
Languages : en
Pages : 0
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Book Description
A central problem in planning production capacity is how to effectively manage demand risk. We develop a model that integrates capacity planning and risk hedging decisions under a popular risk measure, conditional value at risk (CVaR). The CVaR objective generalizes the usual risk-neutral objective (such as the expected payoff), and allows for explicit modeling of the degree of aversion to downside risk (associated with low demand). The starting point of our model is to incorporate the impact on demand from a financial asset (including, for instance, a tradable market index as a proxy for the general economy) via a demand rate function. This way, in addition to the capacity decision at the beginning of the planning horizon, there is also a dynamic hedging strategy throughout the horizon, the latter plays the role of both mitigating demand risk and supplementing the payoff. The hedging strategy is restricted to partial information along with a cap on loss (pathwise). To find the optimal hedging strategy, we construct and solve a dual problem to derive the optimal terminal wealth from hedging; the real-time hedging strategy is then mapped out via the martingale representation theorem. With the hedging strategy optimized, we show that optimizing the production quantity is a concave maximization problem. With both production and hedging (jointly) optimized, we provide a complete characterization of the efficient frontier, and quantify the improvement over the production-only approach. Furthermore, via sensitivity and asymptotic analyses, we spell out the impacts of the hedging budget and the risk aversion level, along with other qualitative insights.
Author: Liao Wang
Publisher:
ISBN:
Category :
Languages : en
Pages : 49
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Book Description
We study production planning integrated with risk hedging by considering shortfall as the risk measure. In addition to the one-time production quantity decision, there is a real-time hedging strategy throughout the horizon; and the goal is to minimize the gap between a pre-specified target and the total terminal wealth achieved by both production and hedging. We assume partial information -- hedging is executed based on information from the financial market only, and impose a budget constraint to cap any loss from hedging. To find the optimal hedging strategy, we construct a dual problem, which provides a lower bound to the original problem. Solving the lower-bound problem yields the optimal terminal wealth from hedging; the real-time hedging strategy is then mapped out via martingale representation theorem. Interestingly, the optimal hedging strategy takes the form of a portfolio of two options, a digital option and a put option. With the hedging strategy optimized, we show that optimizing production quantity is a convex minimization problem. With both production and hedging optimized, we provide a complete characterization of the efficient frontier: the minimized shortfall as an increasing function of the target. We also derive an explicit quantification of the shortfall reduction achieved by hedging. Asymptotic analysis on several key parameters (such as the target, the budget, and the production quantity) generates additional insights to the hedging strategy and its impact.
Author: Liao Wang
Publisher:
ISBN:
Category :
Languages : en
Pages :
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Book Description
By quantifying the risk reduction contributed by the hedging strategy, we demonstrate its substantial improvement over a production-only decision. To derive the mean-variance hedging strategy, we use a numeraire-based approach, and the derived optimal strategy consists of a risk mitigation component and an investment component. For the shortfall hedging, a convex duality method is used, and the optimal strategy takes the form of a put option and a digital option, which combine to close the gap from the target left by production (only). Furthermore, we extend the models and results by allowing multiple products, with demand rates depending on multiple assets. We also make extension by allowing the asset price to follow various stochastic processes (other than the geometric Brownian motion).
Author: Institute for Operations Research and the Management Sciences. National Meeting
Publisher:
ISBN:
Category : Industrial management
Languages : en
Pages : 644
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Book Description
Author: Panos Kouvelis
Publisher: John Wiley & Sons
ISBN: 1118115791
Category : Business & Economics
Languages : en
Pages : 497
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Book Description
A comprehensive, one-stop reference for cutting-edge research in integrated risk management, modern applications, and best practices In the field of business, the ever-growing dependency on global supply chains has created new challenges that traditional risk management must be equipped to handle. Handbook of Integrated Risk Management in Global Supply Chains uses a multi-disciplinary approach to present an effective way to manage complex, diverse, and interconnected global supply chain risks. Contributions from leading academics and researchers provide an action-based framework that captures real issues, implementation challenges, and concepts emerging from industry studies.The handbook is divided into five parts: Foundations and Overview introduces risk management and discusses the impact of supply chain disruptions on corporate performance Integrated Risk Management: Operations and Finance Interface explores the joint use of operational and financial hedging of commodity price uncertainties Supply Chain Finance discusses financing alternatives and the role of financial services in procurement contracts; inventory management and capital structure; and bank financing of inventories Operational Risk Management Strategies outlines supply risks and challenges in decentralized supply chains, such as competition and misalignment of incentives between buyers and suppliers Industrial Applications presents examples and case studies that showcase the discussed methodologies Each topic's presentation includes an introduction, key theories, formulas, and applications. Discussions conclude with a summary of the main concepts, a real-world example, and professional insights into common challenges and best practices. Handbook of Integrated Risk Management in Global Supply Chains is an essential reference for academics and practitioners in the areas of supply chain management, global logistics, management science, and industrial engineering who gather, analyze, and draw results from data. The handbook is also a suitable supplement for operations research, risk management, and financial engineering courses at the upper-undergraduate and graduate levels.
Author: Johan Hagenbjörk
Publisher: Linköping University Electronic Press
ISBN: 917929927X
Category :
Languages : sv
Pages : 129
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Book Description
The global fixed income market is an enormous financial market whose value by far exceeds that of the public stock markets. The interbank market consists of interest rate derivatives, whose primary purpose is to manage interest rate risk. The credit market primarily consists of the bond market, which links investors to companies, institutions, and governments with borrowing needs. This dissertation takes an optimization perspective upon modeling both these areas of the fixed-income market. Legislators on the national markets require financial actors to value their financial assets in accordance with market prices. Thus, prices of many assets, which are not publicly traded, must be determined mathematically. The financial quantities needed for pricing are not directly observable but must be measured through solving inverse optimization problems. These measurements are based on the available market prices, which are observed with various degrees of measurement noise. For the interbank market, the relevant financial quantities consist of term structures of interest rates, which are curves displaying the market rates for different maturities. For the bond market, credit risk is an additional factor that can be modeled through default intensity curves and term structures of recovery rates in case of default. By formulating suitable optimization models, the different underlying financial quantities can be measured in accordance with observable market prices, while conditions for economic realism are imposed. Measuring and managing risk is closely connected to the measurement of the underlying financial quantities. Through a data-driven method, we can show that six systematic risk factors can be used to explain almost all variance in the interest rate curves. By modeling the dynamics of these six risk factors, possible outcomes can be simulated in the form of term structure scenarios. For short-term simulation horizons, this results in a representation of the portfolio value distribution that is consistent with the realized outcomes from historically observed term structures. This enables more accurate measurements of interest rate risk, where our proposed method exhibits both lower risk and lower pricing errors compared to traditional models. We propose a method for decomposing changes in portfolio values for an arbitrary portfolio into the risk factors that affect the value of each instrument. By demonstrating the method for the six systematic risk factors identified for the interbank market, we show that almost all changes in portfolio value and portfolio variance can be attributed to these risk factors. Additional risk factors and approximation errors are gathered into two terms, which can be studied to ensure the quality of the performance attribution, and possibly improve it. To eliminate undesired risk within trading books, banks use hedging. Traditional methods do not take transaction costs into account. We, therefore, propose a method for managing the risks in the interbank market through a stochastic optimization model that considers transaction costs. This method is based on a scenario approximation of the optimization problem where the six systematic risk factors are simulated, and the portfolio variance is weighted against the transaction costs. This results in a method that is preferred over the traditional methods for all risk-averse investors. For the credit market, we use data from the bond market in combination with the interbank market to make accurate measurements of the financial quantities. We address the notoriously difficult problem of separating default risk from recovery risk. In addition to the previous identified six systematic risk factors for risk-free interests, we identify four risk factors that explain almost all variance in default intensities, while a single risk factor seems sufficient to model the recovery risk. Overall, this is a higher number of risk factors than is usually found in the literature. Through a simple model, we can measure the variance in bond prices in terms of these systematic risk factors, and through performance attribution, we relate these values to the empirically realized variances from the quoted bond prices. De globala ränte- och kreditmarknaderna är enorma finansiella marknader vars sammanlagda värden vida överstiger de publika aktiemarknadernas. Räntemarknaden består av räntederivat vars främsta användningsområde är hantering av ränterisker. Kreditmarknaden utgörs i första hand av obligationsmarknaden som syftar till att förmedla pengar från investerare till företag, institutioner och stater med upplåningsbehov. Denna avhandling fokuserar på att utifrån ett optimeringsperspektiv modellera både ränte- och obligationsmarknaden. Lagstiftarna på de nationella marknaderna kräver att de finansiella aktörerna värderar sina finansiella tillgångar i enlighet med marknadspriser. Därmed måste priserna på många instrument, som inte handlas publikt, beräknas matematiskt. De finansiella storheter som krävs för denna prissättning är inte direkt observerbara, utan måste mätas genom att lösa inversa optimeringsproblem. Dessa mätningar görs utifrån tillgängliga marknadspriser, som observeras med varierande grad av mätbrus. För räntemarknaden utgörs de relevanta finansiella storheterna av räntekurvor som åskådliggör marknadsräntorna för olika löptider. För obligationsmarknaden utgör kreditrisken en ytterligare faktor som modelleras via fallissemangsintensitetskurvor och kurvor kopplade till förväntat återvunnet kapital vid eventuellt fallissemang. Genom att formulera lämpliga optimeringsmodeller kan de olika underliggande finansiella storheterna mätas i enlighet med observerbara marknadspriser samtidigt som ekonomisk realism eftersträvas. Mätning och hantering av risker är nära kopplat till mätningen av de underliggande finansiella storheterna. Genom en datadriven metod kan vi visa att sex systematiska riskfaktorer kan användas för att förklara nästan all varians i räntekurvorna. Genom att modellera dynamiken i dessa sex riskfaktorer kan tänkbara utfall för räntekurvor simuleras. För kortsiktiga simuleringshorisonter resulterar detta i en representation av fördelningen av portföljvärden som väl överensstämmer med de realiserade utfallen från historiskt observerade räntekurvor. Detta möjliggör noggrannare mätningar av ränterisk där vår föreslagna metod uppvisar såväl lägre risk som mindre prissättningsfel jämfört med traditionella modeller. Vi föreslår en metod för att dekomponera portföljutvecklingen för en godtycklig portfölj till de riskfaktorer som påverkar värdet för respektive instrument. Genom att demonstrera metoden för de sex systematiska riskfaktorerna som identifierats för räntemarknaden visar vi att nästan all portföljutveckling och portföljvarians kan härledas till dessa riskfaktorer. Övriga riskfaktorer och approximationsfel samlas i två termer, vilka kan användas för att säkerställa och eventuellt förbättra kvaliteten i prestationshärledningen. För att eliminera oönskad risk i sina tradingböcker använder banker sig av hedging. Traditionella metoder tar ingen hänsyn till transaktionskostnader. Vi föreslår därför en metod för att hantera riskerna på räntemarknaden genom en stokastisk optimeringsmodell som också tar hänsyn till transaktionskostnader. Denna metod bygger på en scenarioapproximation av optimeringsproblemet där de sex systematiska riskfaktorerna simuleras och portföljvariansen vägs mot transaktionskostnaderna. Detta resulterar i en metod som, för alla riskaverta investerare, är att föredra framför de traditionella metoderna. På kreditmarknaden använder vi data från obligationsmarknaden i kombination räntemarknaden för att göra noggranna mätningar av de finansiella storheterna. Vi angriper det erkänt svåra problemet att separera fallissemangsrisk från återvinningsrisk. Förutom de tidigare sex systematiska riskfaktorerna för riskfri ränta, identifierar vi fyra riskfaktorer som förklarar nästan all varians i fallissemangsintensiteter, medan en enda riskfaktor tycks räcka för att modellera återvinningsrisken. Sammanlagt är detta ett större antal riskfaktorer än vad som brukar användas i litteraturen. Via en enkel modell kan vi mäta variansen i obligationspriser i termer av dessa systematiska riskfaktorer och genom prestationshärledningen relatera dessa värden till de empiriskt realiserade varianserna från kvoterade obligationspriser.
Author: Lima Zhao
Publisher: Springer
ISBN: 3319766635
Category : Business & Economics
Languages : en
Pages : 192
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Book Description
This textbook presents a coherent and robust structure for integrated risk management in the context of operations and finance. It explains how the operations-finance interface jointly optimizes material and financial flows under intricate risk exposures. The book covers financial flexibility, operational hedging, enterprise risk management (ERM), supply chain risk management (SCRM), integrated risk management (IRM), supply chain finance (SCF), and financial management of supply chain strategies. Both qualitative and quantitative approaches – including conceptualization, theory building, analytical modeling, and empirical research – are used to assess the value creation by integrating operations and finance. “This book provides a comprehensive description of the interactions between finance and operations and of how managers can best make decisions in recognition of these effects.” John R. Birge, University of Chicago“Supply chain finance is an emerging area where innovations can unlock great values to complement the advances in information and physical flows of supply chain.” Hau L. Lee, Stanford University“This book provides an excellent overview of supply chain finance and its most recent advances.” Jan A. Van Mieghem, Northwestern University“This book is indispensable for advanced students as well as practitioners when looking for a pedagogical sound and scientific rigorous approach to Supply Chain Finance.” Ralf W. Seifert, IMD/EPFL“The book advances our knowledge on the interface between operations and finance and provides managerial guidelines for effective risk management in the supply chain.” Xiande Zhao, CEIBS
Author: B.N. Pshenichnyi
Publisher: CRC Press
ISBN: 1000105482
Category : Mathematics
Languages : en
Pages : 248
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Book Description
This book presents a theory of necessary conditions for an extremum, including formal conditions for an extremum and computational methods. It states the general results of the theory and shows how these results can be particularized to specific problems.
Author: Nallapaneni Manoj Kumar
Publisher: Frontiers Media SA
ISBN: 2832523285
Category : Technology & Engineering
Languages : en
Pages : 287
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Book Description
Author: Volodymyr Babich
Publisher:
ISBN: 9781680837964
Category : Technology & Engineering
Languages : en
Pages : 218
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Book Description
This monograph, as entitled, defines and describes the research field at the interface of Finance, Operations, and Risk Management (iFORM), provides examples where operations and finance overlap in meaningful ways, outlines promising research directions, and reduces the entry cost for anyone who would like to explore this new and exciting research field. The intended audience for this article includes both PhD students in operations management (OM), finance, and economics, who are looking for dissertation topics, and experienced researchers looking for novel applications of their expertise. The following outlines the rest of this article. Chapter 2 compares perspectives of finance and operations on the same topic: the firm. This motivates the key questions in finance, which is presented in the finance primer in chapter 3 and key questions in OM, which is presented in the OM primer in chapter 4. Having discussed key ideas from these disciplines separately, chapter 5 examines how OM and finance intersect in meaningful ways and suggest several promising research directions. Chapter 6 presents a "dos and don'ts list for publishing and reviewing iFORM papers.
